dc.contributor.author | Mastorocostas, Paris | |
dc.contributor.author | Hilas, Constantinos | |
dc.date.accessioned | 2015-06-19T21:49:34Z | |
dc.date.available | 2015-06-19T21:49:34Z | |
dc.date.issued | 2012-02 | |
dc.identifier.other | http://www.sciencedirect.com/science/article/pii/S0952197611000649 | el |
dc.identifier.uri | http://apothesis.teicm.gr/xmlui/handle/123456789/1391 | |
dc.description.abstract | In this work a computational intelligence-based approach is proposed for forecasting outgoing telephone calls in a University Campus. A modified Takagi–Sugeno–Kang fuzzy neural system is presented, where the consequent parts of the fuzzy rules are neural networks with an internal recurrence, thus introducing the dynamics to the overall system. The proposed model, entitled Locally Recurrent Neurofuzzy Forecasting System (LR-NFFS), is compared to well-established forecasting models, where its particular characteristics are highlighted. | en |
dc.format.extent | 7 | el |
dc.language.iso | en | el |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | A computational intelligence-based forecasting system for telecommunications time series | en |
dc.type | Άρθρο σε επιστημονικό περιοδικό | el |
dc.identifier.doi | 10.1016/j.engappai.2011.04.004 | |
dc.publication.category | Απαγόρευση δημοσίευσης - Βιβλιογραφική αναφορά | el |
dc.relation.journal | Engineering Applications of Artificial Intelligence;Vol. 25, Iss. 1 | |
dc.subject.keyword | Dynamic TSK fuzzy neural system | el |
dc.subject.keyword | Internal feedback | el |
dc.subject.keyword | Telecommunications data | el |
dc.subject.keyword | Non-linear time series forecasting | el |
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